Transcript CS206 --- Electronic Commerce

Transactions, Views, Indexes
Controlling Concurrent Behavior
Virtual and Materialized Views
Speeding Accesses to Data
1
Why Transactions?
Database systems are normally being
accessed by many users or processes at
the same time.
 Both queries and modifications.
Unlike operating systems, which
support interaction of processes, a
DMBS needs to keep processes from
troublesome interactions.
2
Example: Bad Interaction
You and your domestic partner each
take $100 from different ATM’s at
about the same time.
 The DBMS better make sure one account
deduction doesn’t get lost.
Compare: An OS allows two people to
edit a document at the same time. If
both write, one’s changes get lost.
3
Transactions
Transaction = process involving
database queries and/or modification.
Normally with some strong properties
regarding concurrency.
Formed in SQL from single statements
or explicit programmer control.
4
ACID Transactions
ACID transactions are:
 Atomic : Whole transaction or none is done.
 Consistent : Database constraints preserved.
 Isolated : It appears to the user as if only one
process executes at a time.
 Durable : Effects of a process survive a crash.
Optional: weaker forms of transactions are
often supported as well.
5
COMMIT
The SQL statement COMMIT causes a
transaction to complete.
 It’s database modifications are now
permanent in the database.
6
ROLLBACK
The SQL statement ROLLBACK also
causes the transaction to end, but by
aborting.
 No effects on the database.
Failures like division by 0 or a
constraint violation can also cause
rollback, even if the programmer does
not request it.
7
Example: Interacting Processes
Assume the usual Sells(bar,beer,price)
relation, and suppose that Joe’s Bar sells
only Bud for $2.50 and Miller for $3.00.
Sally is querying Sells for the highest and
lowest price Joe charges.
Joe decides to stop selling Bud and
Miller, but to sell only Heineken at $3.50.
8
Sally’s Program
Sally executes the following two SQL
statements called (min) and (max) to
help us remember what they do.
(max)
SELECT MAX(price) FROM Sells
WHERE bar = ’Joe’’s Bar’;
(min)
SELECT MIN(price) FROM Sells
WHERE bar = ’Joe’’s Bar’;
9
Joe’s Program
At about the same time, Joe executes the
following steps: (del) and (ins).
(del) DELETE FROM Sells
WHERE bar = ’Joe’’s Bar’;
(ins) INSERT INTO Sells
VALUES(’Joe’’s Bar’, ’Heineken’,
3.50);
10
Interleaving of Statements
Although (max) must come before
(min), and (del) must come before
(ins), there are no other constraints on
the order of these statements, unless
we group Sally’s and/or Joe’s
statements into transactions.
11
Example: Strange Interleaving
Suppose the steps execute in the order
(max)(del)(ins)(min).
{3.50}
Joe’s Prices: {2.50,3.00} {2.50,3.00}
(max)
(del)
(ins)
(min)
Statement:
3.00
3.50
Result:
Sally sees MAX < MIN!
12
Fixing the Problem by Using
Transactions
If we group Sally’s statements
(max)(min) into one transaction, then
she cannot see this inconsistency.
She sees Joe’s prices at some fixed
time.
 Either before or after he changes prices, or
in the middle, but the MAX and MIN are
computed from the same prices.
13
Another Problem: Rollback
Suppose Joe executes (del)(ins), not as
a transaction, but after executing these
statements, thinks better of it and
issues a ROLLBACK statement.
If Sally executes her statements after
(ins) but before the rollback, she sees a
value, 3.50, that never existed in the
database.
14
Solution
If Joe executes (del)(ins) as a
transaction, its effect cannot be seen by
others until the transaction executes
COMMIT.
 If the transaction executes ROLLBACK
instead, then its effects can never be
seen.
15
Isolation Levels
SQL defines four isolation levels =
choices about what interactions are
allowed by transactions that execute at
about the same time.
Only one level (“serializable”) = ACID
transactions.
Each DBMS implements transactions in
its own way.
16
Choosing the Isolation Level
 Within a transaction, we can say:
SET TRANSACTION ISOLATION LEVEL X
where X =
1.
2.
3.
4.
SERIALIZABLE
REPEATABLE READ
READ COMMITTED
READ UNCOMMITTED
17
Serializable Transactions
If Sally = (max)(min) and Joe =
(del)(ins) are each transactions, and
Sally runs with isolation level
SERIALIZABLE, then she will see the
database either before or after Joe
runs, but not in the middle.
18
Isolation Level Is Personal Choice
Your choice, e.g., run serializable,
affects only how you see the database,
not how others see it.
Example: If Joe Runs serializable, but
Sally doesn’t, then Sally might see no
prices for Joe’s Bar.
 i.e., it looks to Sally as if she ran in the
middle of Joe’s transaction.
19
Read-Commited Transactions
If Sally runs with isolation level READ
COMMITTED, then she can see only
committed data, but not necessarily the
same data each time.
Example: Under READ COMMITTED,
the interleaving (max)(del)(ins)(min) is
allowed, as long as Joe commits.
 Sally sees MAX < MIN.
20
Repeatable-Read Transactions
Requirement is like read-committed,
plus: if data is read again, then
everything seen the first time will be
seen the second time.
 But the second and subsequent reads may
see more tuples as well.
21
Example: Repeatable Read
Suppose Sally runs under REPEATABLE
READ, and the order of execution is
(max)(del)(ins)(min).
 (max) sees prices 2.50 and 3.00.
 (min) can see 3.50, but must also see 2.50
and 3.00, because they were seen on the
earlier read by (max).
22
Read Uncommitted
A transaction running under READ
UNCOMMITTED can see data in the
database, even if it was written by a
transaction that has not committed
(and may never).
Example: If Sally runs under READ
UNCOMMITTED, she could see a price
3.50 even if Joe later aborts.
23
Views
 A view is a relation defined in terms
of stored tables (called base tables )
and other views.
 Two kinds:
1. Virtual = not stored in the database; just
a query for constructing the relation.
2. Materialized = actually constructed and
stored.
24
Declaring Views
Declare by:
CREATE [MATERIALIZED] VIEW
<name> AS <query>;
Default is virtual.
25
Example: View Definition
CanDrink(drinker, beer) is a view “containing”
the drinker-beer pairs such that the drinker
frequents at least one bar that serves the beer:
CREATE VIEW CanDrink AS
SELECT drinker, beer
FROM Frequents, Sells
WHERE Frequents.bar = Sells.bar;
26
Example: Accessing a View
Query a view as if it were a base table.
 Also: a limited ability to modify views if it
makes sense as a modification of one
underlying base table.
Example query:
SELECT beer FROM CanDrink
WHERE drinker = ’Sally’;
27
Triggers on Views
Generally, it is impossible to modify a
virtual view, because it doesn’t exist.
But an INSTEAD OF trigger lets us
interpret view modifications in a way
that makes sense.
Example: View Synergy has (drinker,
beer, bar) triples such that the bar
serves the beer, the drinker frequents
the bar and likes the beer.
28
Example: The View
Pick one copy of
each attribute
CREATE VIEW Synergy AS
SELECT Likes.drinker, Likes.beer, Sells.bar
FROM Likes, Sells, Frequents
WHERE Likes.drinker = Frequents.drinker
AND Likes.beer = Sells.beer
AND Sells.bar = Frequents.bar;
Natural join of Likes,
Sells, and Frequents
29
Interpreting a View Insertion
We cannot insert into Synergy --- it is a
virtual view.
But we can use an INSTEAD OF trigger
to turn a (drinker, beer, bar) triple into
three insertions of projected pairs, one
for each of Likes, Sells, and Frequents.
 Sells.price will have to be NULL.
30
The Trigger
CREATE TRIGGER ViewTrig
INSTEAD OF INSERT ON Synergy
REFERENCING NEW ROW AS n
FOR EACH ROW
BEGIN
INSERT INTO LIKES VALUES(n.drinker, n.beer);
INSERT INTO SELLS(bar, beer) VALUES(n.bar, n.beer);
INSERT INTO FREQUENTS VALUES(n.drinker, n.bar);
END;
31
Materialized Views
Problem: each time a base table
changes, the materialized view may
change.
 Cannot afford to recompute the view with
each change.
Solution: Periodic reconstruction of the
materialized view, which is otherwise
“out of date.”
32
Example: Axess/Class Mailing List
The class mailing list cs145-aut0708students is in effect a materialized view
of the class enrollment in Axess.
Actually updated four times/day.
 You can enroll and miss an email sent out
after you enroll.
33
Example: A Data Warehouse
Wal-Mart stores every sale at every
store in a database.
Overnight, the sales for the day are
used to update a data warehouse =
materialized views of the sales.
The warehouse is used by analysts to
predict trends and move goods to
where they are selling best.
34
Indexes
Index = data structure used to speed
access to tuples of a relation, given
values of one or more attributes.
Could be a hash table, but in a DBMS it
is always a balanced search tree with
giant nodes (a full disk page) called a
B-tree.
35
Declaring Indexes
No standard!
Typical syntax:
CREATE INDEX BeerInd ON
Beers(manf);
CREATE INDEX SellInd ON
Sells(bar, beer);
36
Using Indexes
Given a value v, the index takes us to
only those tuples that have v in the
attribute(s) of the index.
Example: use BeerInd and SellInd to
find the prices of beers manufactured
by Pete’s and sold by Joe. (next slide)
37
Using Indexes --- (2)
SELECT price FROM Beers, Sells
WHERE manf = ’Pete’’s’ AND
Beers.name = Sells.beer AND
bar = ’Joe’’s Bar’;
1. Use BeerInd to get all the beers made
by Pete’s.
2. Then use SellInd to get prices of those
beers, with bar = ’Joe’’s Bar’
38
Database Tuning
A major problem in making a database
run fast is deciding which indexes to
create.
Pro: An index speeds up queries that can
use it.
Con: An index slows down all
modifications on its relation because the
index must be modified too.
39
Example: Tuning
 Suppose the only things we did with
our beers database was:
1. Insert new facts into a relation (10%).
2. Find the price of a given beer at a given
bar (90%).
 Then SellInd on Sells(bar, beer) would
be wonderful, but BeerInd on
Beers(manf) would be harmful.
40
Tuning Advisors
 A major research thrust.
 Because hand tuning is so hard.
 An advisor gets a query load, e.g.:
1. Choose random queries from the history
of queries run on the database, or
2. Designer provides a sample workload.
41
Tuning Advisors --- (2)
The advisor generates candidate
indexes and evaluates each on the
workload.
 Feed each sample query to the query
optimizer, which assumes only this one
index is available.
 Measure the improvement/degradation in
the average running time of the queries.
42